In [3]:
pip install plotly pip install cufflinks pip install chart_studio

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Requirement already satisfied: pip in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (20.2.3)
Requirement already satisfied: install in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (1.3.4)
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Requirement already satisfied: requests in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (from chart_studio) (2.24.0)Note: you may need to restart the kernel to use updated packages.

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Requirement already satisfied: pycparser in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (from cffi>=1.0.0->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.20)
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In [4]:
pip install seaborn

Requirement already satisfied: seaborn in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (0.11.0)
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Note: you may need to restart the kernel to use updated packages.
In [5]:
pip install matplotlib

Requirement already satisfied: matplotlib in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (3.3.2)
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Requirement already satisfied: six>=1.5 in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)
Note: you may need to restart the kernel to use updated packages.
In [6]:
pip install pandas

Requirement already satisfied: pandas in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (1.1.3)
Requirement already satisfied: pytz>=2017.2 in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (from pandas) (2020.1)
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Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: six>=1.5 in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)
In [7]:
pip install numpy

Requirement already satisfied: numpy in c:\users\naray\anaconda3\envs\plotly\lib\site-packages (1.19.2)Note: you may need to restart the kernel to use updated packages.
In [8]:
import pandas as pd
import numpy as np
import chart_studio.plotly as py
import cufflinks as cf
import seaborn as sns
import plotly.express as px
%matplotlib inline

from plotly.offline import download_plotlyjs, init_notebook_mode,plot ,plot, iplot
init_notebook_mode(connected=True)

cf.go_offline()
In [9]:
#BASICS
In [10]:
arr = np.random.rand(50,4)
df_1 = pd.DataFrame(arr,columns=['A','B','C','D'])
df_1.head()
df_1.iplot()
In [11]:
#lINE PLOTS
In [12]:
import plotly.graph_objects as go
df_stocks = px.data.stocks()
px.line(df_stocks, x ='date',y = 'GOOG',labels = {'x':'Date','y':'Price'})

px.line(df_stocks, x = 'date',y = ['GOOG','AAPL'], labels ={'x':'Date','y':'Price'},title = "Apple Vs Google")

fig = go.Figure()
fig.add_trace(go.Scatter(x = df_stocks.date, y = df_stocks.AAPL,mode="lines", name="Apple"))
fig.add_trace(go.Scatter(x = df_stocks.date, y = df_stocks.AMZN,mode="lines+markers", name="amazon"))
fig.add_trace(go.Scatter(x = df_stocks.date, y = df_stocks.GOOG,mode="lines", name="Google", line = dict(color='Firebrick',width=2,dash="dashdot")))
In [13]:
fig.update_layout(title="STOCK price data 2018-20",xaxis_title="Price",yaxis_title="Date")
fig.update_layout(
xaxis = dict(
showline=True,showgrid=False,showticklabel=True,
linecolor = 'rgb(204,204,204)',
linewidht=2, tickfont = dict(
family="Arial",size=12,color='rgb(82,82,82)',
),
),
yaxis= dict(showgrid= False,zeroline=False, showline =False, showticklabels = False),
autosize =False,
margin=dict(
autoexpand = False,1=100,r=20,t=110,),
showlegend =False,plot_bgcolor='white')

  File "<ipython-input-13-770f4f1109b9>", line 13
    autoexpand = False,1=100,r=20,t=110,),
                      ^
SyntaxError: keyword can't be an expression
In [14]:
#bar chart
In [15]:
df_us = px.data.gapminder().query("country =='India'")
px.bar(df_us, x ='year',y='pop')
In [16]:
df_tips = px.data.tips()
In [17]:
px.bar(df_tips, x='day',y='tip',color='sex',title='tips by sex on each day',labels={'tip':'Tip ammount','day':'Day of the Week'})
px.bar(df_tips, x='sex',y='total_bill',color='smoker',barmode='group')
In [18]:
df_europe = px.data.gapminder().query("continent == 'Europe' and year == 2007 and pop>2.e6")
fig = px.bar(df_europe , y='pop', x='country', text ='pop', color = 'country')

fig
In [19]:
fig.update_traces(texttemplate = '%{text:.2s}',textposition='outside')
fig.update_layout(uniformtext_minsize=8)
fig.update_layout(xaxis_tickangle=-45)
In [20]:
#SCATTER PLOT
In [21]:
df_iris = px.data.iris()
px.scatter(df_iris, x='sepal_width',y='sepal_length',color='species',size='petal_length',hover_data=['petal_width'])
fig = go.Figure()
fig.add_trace(go.Scatter(x = df_iris.sepal_width, y=df_iris.sepal_length,mode='markers',
                         marker_color= df_iris.sepal_width,
                         text = df_iris.species,marker=dict(showscale=True)))
fig.update_traces(marker_line_width=2,marker_size=10)
In [22]:
fig = go.Figure(data=go.Scattergl(
x = np.random.randn(100000),
y = np.random.randn(100000),
mode ='markers',
marker = dict(
color = np.random.randn(100000),
colorscale='Viridis',
line_width=1)))
fig
In [23]:
#pie Charts
In [24]:
df_asia= px.data.gapminder().query("continent == 'Asia' and year == 2007 ")
px.pie(df_asia,values='pop',names='country',title='population of asian continent',color_discrete_sequence =px.colors.sequential.RdBu)
In [25]:
colors= ['blue','green','black','purple','red','brown']
fig = go.Figure(data=[go.Pie(labels= ['Water','Grass','Normal','Pyschic','Fire','Ground'],
                             values =[110,90,80,70,60])])
fig.update_traces(hoverinfo="label+percent",textfont_size=20,textinfo='label+percent',pull=[0.1,0.2,0,0,0,0],marker=dict(colors=colors, line= dict(color="#FFFFFF",width=2)))
fig
In [26]:
#histograms
In [27]:
dice_1 = np.random.randint(1,7,5000)
dice_2 = np.random.randint(1,7,5000)
dice_sum = dice_1 + dice_2
fig = px.histogram(dice_sum,nbins=11,labels={'value':'Dice roll'},title='5000 Dice Roll Histogram',marginal='violin',color_discrete_sequence=['green'])
fig
In [28]:
fig.update_layout(
xaxis_title_text = "Dice ROll",
yaxis_title_text = "Dice Sum ",
bargap =0.2, showlegend =False)
fig
In [29]:
df_tips = px.data.tips()
px.histogram(df_tips,x='total_bill',color = 'sex')
In [30]:
#BOX PLOTS
In [31]:
df_tips = px.data.tips()
In [34]:
px.box(df_tips,x ='sex',y = 'tip',points = 'all')
px.box(df_tips,x = 'day',y ='tip',color='sex')
In [43]:
fig = go.Figure()
fig.add_trace(go.Box(x=df_tips.sex,y =df_tips.tip,marker_color='blue',boxmean='sd'))
In [46]:
df_stocks = px.data.stocks()
fig = go.Figure()
fig.add_trace(go.Box(y=df_stocks.GOOG,boxpoints='all',fillcolor='blue',jitter=0.5,whiskerwidth=0.2))
fig.add_trace(go.Box(y=df_stocks.AAPL,boxpoints='all',fillcolor='red',jitter=0.5,whiskerwidth=0.2))
fig.update_layout(title='Google vs Apple',yaxis = dict(gridcolor='rgb(255,255,255)',gridwidth =3),paper_bgcolor='rgb(243,243,243)',plot_bgcolor ='rgb(243,243,243)')
In [47]:
#voilin plots
In [49]:
df_tips = px.data.tips()
px.violin(df_tips,y='total_bill',box=True,points='all')
In [52]:
px.violin(df_tips,y='tip',x='smoker',color='sex',box=True,points='all',hover_data=df_tips.columns)
In [58]:
px.violin(df_tips,y='tip',x='smoker',color='sex',box=True,points='all',hover_data=df_tips.columns)
fig = go.Figure()
fig.add_trace(go.Violin(x =df_tips['day'][df_tips['smoker']=='Yes'],y=df_tips['total_bill'][df_tips['smoker']=='Yes'],legendgroup='Yes',scalegroup='Yes',name='Yes',side='positive',line_color='Blue'))
fig.add_trace(go.Violin(x =df_tips['day'][df_tips['smoker']=='No'],y=df_tips['total_bill'][df_tips['smoker']=='No'],legendgroup='Yes',scalegroup='Yes',name='No',side='positive',line_color='Red'))
In [59]:
flights = sns.load_dataset("flights")
flights
Out[59]:
year month passengers
0 1949 Jan 112
1 1949 Feb 118
2 1949 Mar 132
3 1949 Apr 129
4 1949 May 121
... ... ... ...
139 1960 Aug 606
140 1960 Sep 508
141 1960 Oct 461
142 1960 Nov 390
143 1960 Dec 432

144 rows × 3 columns

In [63]:
fig = px.density_heatmap(flights, x ="year",y ="month",z ='passengers',color_continuous_scale='Viridis')
fig
In [64]:
fig = px.density_heatmap(flights, x ="year",y ="month",z ='passengers',marginal_x ='histogram',marginal_y ='histogram')
fig
In [66]:
#3D scatter plot
In [68]:
fig = px.scatter_3d(flights, x ="year",y ="month",z ='passengers',color='year',opacity=0.7)
fig
In [70]:
fig = px.line_3d(flights, x ="year",y ="month",z ='passengers',color='year')
fig
In [71]:
#scatter matrices
In [72]:
fig = px.scatter_matrix(flights,color='month')
fig
In [73]:
#map scatter plots
In [74]:
df = px.data.gapminder().query('year == 2007')
fig =px.scatter_geo(df,locations ='iso_alpha',hover_name='country',size='pop',projection='orthographic')
fig
In [76]:
#polar charts
In [82]:
df_wind = px.data.wind()
px.scatter_polar(df_wind, r= 'frequency',theta = 'direction',color='strength',size='frequency',symbol='strength')
In [84]:
df_wind = px.data.wind()
px.line_polar(df_wind, r= 'frequency',theta = 'direction',color='strength',line_close=True,template="plotly_dark")
In [85]:
#ternary plot
In [87]:
df_exp = px.data.experiment()
px.scatter_ternary(df_exp, a='experiment_1',b ='experiment_2',c='experiment_3',hover_name='group',color="gender")
In [88]:
#facets
In [91]:
df_tips = px.data.tips()
px.scatter(df_tips,x='total_bill',y='tip',color='smoker',facet_col='sex')
px.histogram(df_tips,x = 'total_bill', y ='tip',color='sex',facet_row='time',facet_col='day',category_orders={'day':["Thur","fri","Sat","Sun"],"time":["Lunch","Dinner"]})
In [94]:
att_df = sns.load_dataset("attention")
fig = px.line(att_df,x="solutions", y = "score",facet_col="subject",facet_col_wrap=5,title="Scores Based on attention")
fig
In [95]:
#animated Plot
In [97]:
df_cnt = px.data.gapminder()
px.scatter(df_cnt,x ="gdpPercap",y ="lifeExp",animation_frame="year",animation_group="country",size="pop",color='continent',hover_name='country',log_x=True,size_max=55,range_x=[100,10000],range_y=[25,90])
In [101]:
px.bar(df_cnt,x='continent',y ='pop',color='continent',animation_frame='year',animation_group='country',range_y=[0,4000000000])
In [ ]: